Fast and Accurate Camera Covariance Computation for Large 3D Reconstruction
This work addresses a computational bottleneck for researchers and practitioners in computer vision and photogrammetry, providing a tool to evaluate reconstruction quality in large-scale settings, though it is incremental as it builds on existing uncertainty propagation methods.
The paper tackles the problem of efficiently computing camera parameter uncertainties in large-scale 3D reconstruction, achieving a tenfold speedup over previous methods and enabling uncertainty estimation for thousands of cameras in tens of seconds on standard hardware.
Estimating uncertainty of camera parameters computed in Structure from Motion (SfM) is an important tool for evaluating the quality of the reconstruction and guiding the reconstruction process. Yet, the quality of the estimated parameters of large reconstructions has been rarely evaluated due to the computational challenges. We present a new algorithm which employs the sparsity of the uncertainty propagation and speeds the computation up about ten times \wrt previous approaches. Our computation is accurate and does not use any approximations. We can compute uncertainties of thousands of cameras in tens of seconds on a standard PC. We also demonstrate that our approach can be effectively used for reconstructions of any size by applying it to smaller sub-reconstructions.